1. Field of the Invention
This invention relates to a method and apparatus for forecasting future values of a time series and particularly for forecasting future values of a time series relating to traffic levels in a communications network.
2. Description of the Prior Art
One approach to the task of trends analysis and making predictions has been to use neural network technology. For example, neural networks have been used to forecast aspects of the financial markets and also in many other situations in which it is required to forecast the future development of a time series. A time series is a sequence of values that are measured over time, typically at fixed time intervals. For example, this could be the temperature of air in a building over time, the number of births in a given city over time, the number of sun spots over time or even the amount of water consumed in a given community. In practice time is usually viewed in terms of discrete time steps, leading to an instance of the temperature of the air (for example) after each of a number of time intervals.
There are a number of problems involved in using neural network technology to predict the future development of a time series. A first problem is how to supply the temporal information to the neural network. Since most neural networks have previously been defined for pattern recognition in static patterns the temporal dimension has to be supplied in an appropriate way. Other problems include the requirements for large data bases of information with which to train the neural network and also the need for careful evaluation of the trained neural network. Both these requirements often prove costly and time consuming. A further problem relates to limitations of the learning algorithms used to train the neural networks. Poor learning algorithms lead to lengthy training times and poor performance of the neural network once it is trained. For example, the neural network may "over fit" the data so that its ability to generalise and cope with previously unseen data is limited. Also, the neural network may simply learn to detect noise in the data rather than more meaningful and useful information.
One application of neural networks to predict time-series development relates to asynchronous transfer mode (ATM) communications networks. ATM technology offers a great flexibility of transmission bandwidth allocation. Using this technology the amount of bandwidth allocated for a particular use can be altered. In order to make good use of this ability it is necessary to predict future bandwidth requirements in order that the amount of bandwidth can be adjusted to meet this future requirement. The prediction process must be able to ensure sufficient bandwidth to provide quality of service for a particular task, whilst at the same time minimising over prediction of bandwidth requirements. This enables the maximum amount of remaining bandwidth to be available for other services. For example, one problem is the prediction of voice traffic on ATM communication networks. In this situation, as much bandwidth as possible should remain at any one time for other services such as video transmission. This is illustrated in FIG. 7.
For predicting voice traffic levels in ATM networks there are several specific problems. For example, relatively short-term prediction must be possible, such as providing an estimate of traffic levels 15 minutes in advance. Also, there are many characteristics of telecommunications traffic that lead to problems specific to this area. For example, one of the characteristics of telecommunications traffic is the superimposition of many cyclical effects which can have different periodicities. For instance, there are hourly trends corresponding to the business day, daily trends (some working days are typically busier than others and weekends have very little traffic), monthly trends and seasonal trends. This means that the prediction process must be able to cope with these cyclical effects as well as underlying trends in the data. One known approach to this problem is to de-trend the data by working out what the periodicities of the cyclical effects are and what is the average effect from each of these influences. The trend(s) are then removed and prediction made on the resulting data. However this is a time consuming and complex process which also leads to inaccuracies in the predictions. Telecommunications is a fast growing area in which traffic behaviour is continually evolving and changing. The prediction process also needs to cope with this evolution as well as interactions between the various effects.
Another problem relates to the early identification of problems in communications networks, and especially ATM networks. ATM networks produce a continually varying and often heavy stream of alarms and other symptomatic information. In this situation it is required to identify when a sequence of events is indicative of an incipient, major component of failure.
A further problem relates to customer network management. Customers who make extensive use of a service providers network are often provided with a "virtual private network". This enables them to control part of the service providers network under a "service level agreement". The service level agreement typically specifies the bandwidth levels that the customer is allowed to use. If this bandwidth level is exceeded at any time by the customer, data can effectively be "lost". However, it is very difficult for the customer to predict bandwidth requirements in advance in order to negotiate for a larger bandwidth when this is required.
It is accordingly an object of the present invention to provide a method and apparatus for forecasting future values of a time series and particularly for forecasting future values of a time series relating to traffic levels in a communications network which overcomes or at least mitigates one or more of the problems noted above.